In this paper, we present a novel method for tomographic image reconstruction in SPECT imaging with a low number of projections. Deep convolutional neural networks (CNN) are employed in the new reconstruction method. Projection data from software phantoms were used to train the CNN network. For evaluation of the efficacy of the proposed method, software phantoms and hardware phantoms based on the FOV SPECT system were used. The resulting tomographic images are compared to those produced by the "Maximum Likelihood Expectation Maximisation" (MLEM).

翻译：在本文中,我们介绍了一种在SPECT成像中进行摄影图像重建的新方法,预测数量较少。在新的重建方法中使用了深演神经网络(CNN)。软件幻影中的预测数据被用于培训CNN网络。在评估拟议方法的有效性时,使用了基于FOV SPECT系统的软件幻影和硬件幻影。由此产生的图像与“最大希望实现最大希望最大化”(MLEM)制作的图像进行了比较。